Below are the updated season forecasts using data from games through October 8.

Power Rankings

The “Power Rankings” we concoct are the “strength” of the team according to its competitive expectations. They are computed by forecasting the expected points (3 x win probability + 1 x draw probability) against every other MLS team – both home and away – and taking the average per team.

SEBA has the Union remaining at 13th.

For those interested in how Philadelphia’s matches are weighted in the model (especially if skeptical about why SEBA’s rankings can be different from other outlets):

‘wght’ is the actual weight value used in the model, which is a combination of the ‘timewght’ (how long ago the match occurred), ‘goalWght’ (how much luck could have influenced the match result, as indicated by the goal differential), and ‘rostWght’ (how similar the roster deployments for both teams were compared with current trends).

By comparison, the current roster expectations for maximum weight for the Union (and therefore the model’s assessment of ‘who’ Philadelphia is in the model) are currently:

The following shows the evolution of SEBA’s power rankings for the MLS Eastern Conference over time:

Playoffs probability and more

Philadelphia’s playoffs odds have decreased from 1.1% to 0%.

The following shows the simulation distribution for the points earned by the sixth place MLS East club as well as the simulation distribution of points that Philadelphia is expected to earn.

In part, clubs that score a lot of goals are given an advantage in MLS Cup due to the two-leg aggregate goal format of the conference semifinals and finals. That gives those clubs a better chance at notching large victories which carry over.

Over time, we can see how Philadelphia’s odds for different prizes have changed:

The following shows the probability of each playoff ranking finish:

The following shows the summary of the simulations in an easy table format:

Next, we show how the Remaining Strength of Schedule affects each team.

The “Points Percentage Advantage” shown on the X-axis represents the percentage of points expected over the league average schedule. This “points expected” value is generated by simulating how all teams would perform with all remaining schedules (and therefore judges a schedule based upon how all teams would perform in that scenario).

In short, the higher the value, the easier the remaining schedule.

Accompanying the advantage percentage in the following table is their current standings rank (right now ties are not properly calculated beyond pts/gd/gf; I may fix that, but maybe not for a while), the remaining home matches, the remaining away matches, the current average points-per-game of future opponents (results-based, not model-based), and the average power ranking of future opponents according to SEBA.

The following shows the expectations for upcoming Philadelphia matches:

Philadelphia +/- Player Analysis

The ‘+’ is a measure counting how many goals were scored by the Union while the player was on the field. The ‘-‘ counts how many goals were scored against the Union while the player was on the field.

Player

Net

+

–

MINS

Net/90

+/90

-/90

1

Chris Pontius

8

32

24

4499

0.160

0.640

0.480

2

Jack Elliott

5

37

32

6104

0.074

0.546

0.472

3

Derrick Jones

4

13

9

1525

0.236

0.767

0.531

4

Giliano Wijnaldum

3

19

16

3087

0.087

0.554

0.466

5

Warren Creavalle

2

8

6

1014

0.178

0.710

0.533

6

Raymon Gaddis

2

28

26

4722

0.038

0.534

0.496

7

Oguchi Onyewu

2

29

27

5040

0.036

0.518

0.482

8

Andre Blake

2

32

30

5580

0.032

0.516

0.484

9

Ilsinho

1

23

22

3231

0.028

0.641

0.613

10

CJ Sapong

1

41

40

7074

0.013

0.522

0.509

11

Alejandro Bedoya

0

35

35

6267

0.000

0.503

0.503

12

Fabian Herbers

0

8

8

660

0.000

1.091

1.091

13

Haris Medunjanin

-1

42

43

7641

-0.012

0.495

0.506

14

Adam Najem

-2

1

3

142

-1.268

0.634

1.901

15

Fabinho

-3

23

26

4406

-0.061

0.470

0.531

16

John McCarthy

-3

10

13

2070

-0.130

0.435

0.565

17

Fafa Picault

-4

25

29

4191

-0.086

0.537

0.623

18

Keegan Rosenberry

-4

14

18

2593

-0.139

0.486

0.625

19

Richie Marquez

-4

13

17

2493

-0.144

0.469

0.614

20

Roland Alberg

-4

13

17

1606

-0.224

0.729

0.953

21

Joshua Yaro

-4

5

9

942

-0.382

0.478

0.860

22

Jay Simpson

-4

5

9

363

-0.992

1.240

2.231

23

Marcus Epps

-5

6

11

921

-0.489

0.586

1.075

Model Validation

The following shows the overall net values since 2013 which is when data is available.

The following shows the degree of error by the model vs the error if the model was purely random without intelligence. The x-axis is based on the date from which the forecast was made (this will update throughout the season as more results are finalized and compared with predictions). The ordinal squared error metric (not a traditional metric) is calculated as:

(ProbW – ActW)^2 + (ProbT – ActT)^2 + (ProbL – ActL)^2 +

((ProbW + ProbT) – (ActW + ActT))^2 +

((ProbL + ProbT) – (ActL + ActT))^2

where Prob[W/T/L] is the model’s probability of resulting outcomes and Act[W/T/L] is a 1 or 0 representation of whether it actually happened.

Random errors will decline when more ties occur as there is a less severe penalty for ties.

We should expect random errors to remain relatively constant over time, where our model’s errors will hopefully decline as the season goes on as it gathers new information.

These data points are not fixed until the end of the season due to additional matches adding to them.

USL

Power Rankings

SEBA has the Bethlehem Steel popping back up from 17th to 15th while it has Harrisburg City remaining at 21st.

The following shows the evolution of SEBA’s power rankings for the USL East over time.

Playoffs probability and more

Bethlehem’s playoff odds have increased from 68.3% to 95.6%.

Bethlehem’s odds at becoming the USL Champion have increased from 1.0% to 2.3%:

Over time, we can see how the odds for different prizes change for Bethlehem and Harrisburg.

The following shows the probability of each post-playoff ranking finish:

The following shows the summary of simulations in an easy table format.

We can also clarify how the Remaining Strength of Schedule affects each team.

The “Points Percentage Advantage” shown on the X-axis represents the percentage of points expected over the league average schedule. This “points expected” value is generated by simulating how all teams would perform with all remaining schedules (and therefore judges a schedule based upon how all teams would perform in that scenario).

In short, the higher the value, the easier the remaining schedule.

Remaining home field advantage will be significant here. It can also be true that a better team has an ‘easier’ schedule simply because they do not have to play themselves. Likewise, a bad team may have a ‘harder’ schedule because they also do not play themselves.

The table following the chart also shares helpful context with these percentages.

Accompanying the advantage percentage in the following table is their current standings rank (right now ties are not properly calculated beyond pts/gd/gf), the remaining home matches, the remaining away matches, the current average points-per-game of future opponents (results-based, not model-based), and the average power ranking of future opponents according to SEBA.

The following shows the expectations for upcoming matches for both Bethlehem and Harrisburg:

Model Validation

This chart is the same as that in the MLS forecast (except for USL matches instead of MLS).

Remember that these data points are not fixed until the end of the season.

NWSL

Power Rankings

Playoffs probability and more

The following shows the summary of the simulations in an easy table format.

Author: Chris Sherman
Chris is a data scientist who was a mediocre left back and center back for the Upper Dublin High School..... JV team. He then got a Computer Science & Business degree from Lehigh University and a Master's' degree from Northwestern University in Predictive Analytics. He likes data and building programs to do his thinking for him.

One Comment

SEBA thinks Bethlehem might finish 6th and fly to Tampa Bay for a playoff game.
.
Hope you’re right, Chris. And that the YouTube broadcast circles back on itself less than last time down there.
.
Could happen if they beat St. Louis Sunday.